Two-dimensional direction-of-arrival estimation method for coprime planar array based on structured coarray tensor processing

ABSTRACT

A two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, the method includes: deploying a coprime planar array; modeling a tensor of the received signals; deriving the second-order equivalent signals of an augmented virtual array based on cross-correlation tensor transformation; deploying a three-dimensional coarray tensor of the virtual array; deploying a five-dimensional coarray tensor based on a coarray tensor dimension extension strategy; forming a structured coarray tensor including three-dimensional spatial information; and achieving two-dimensional direction-of-arrival estimation through CANDECOMP/PARACFAC decomposition. The present disclosure constructs a processing framework of a structured coarray tensor based on statistical analysis of coprime planar array tensor signals, to achieve multi-source two-dimensional direction-of-arrival estimation in the underdetermined case on the basis of ensuring the performance such as resolution and estimation accuracy, and can be used for multi-target positioning.

TECHNICAL FIELD

The present disclosure belongs to the field of array signal processing technologies, and in particular, to a two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor signal processing, and can be used for multi-target positioning.

BACKGROUND

As a typical systematic sparse array architecture, a coprime array can break through the bottleneck of traditional uniform arrays with a limited degrees-of-freedom. In order to increase the degrees-of-freedom, the received signals of the coprime array are generally derived into an augmented virtual array, and the corresponding coarray signals are used for the subsequent processing. In order to improve the degree of freedom for two-dimensional direction-of-arrival estimation, much attention has been paid to the two-dimensional coarray signal processing. In a traditional two-dimensional direction-of-arrival estimation method with the coprime planar array, a common approach is to derive coarray signals by vectorizing the second-order correlation statistics of the coprime array, and then extend the one-dimensional direction-of-arrival estimation method to a two-dimensional/high-dimensional scenarios, so as to achieve direction-of-arrival estimation through further coarray processing. The above approach destroys the multidimensional the original structure of the signals received by the coprime planar array, and the coarray signals derived from vectorization encounter the challenge of large scale and loss of structural information.

Tensor as a multidimensional data format, can be used to preserve the characteristics of the multidimensional signals. For feature analysis of multidimensional signals, high-order singular value decomposition and tensor decomposition methods provide abundant mathematical tools for tensor-based signal processing. In recent years, tensor has been widely applied in array signal processing, image signal processing, statistics, and other fields. Therefore, by using a tensor to represent the received signals of a coprime planar array and the corresponding coarray signals, the multidimensional structural information of signals can be retained effectively, which provides an important theoretical tool for improving the performance of direction-of-arrival estimation. At the same time, it is expected to achieve a breakthrough in the comprehensive performance of direction-of-arrival estimation in terms of resolution, estimation accuracy, and degree of freedom by extending the high-order singular value decomposition and tensor decomposition methods to the coarray domain. However, the coarray tensor-based processing for the coprime planar array has not been discussed in the existing methods, and two-dimensional coarray properties of the coprime planar array are not utilized. Therefore, it is an important problem urgently to be solved to design a direction-of-arrival estimation method with an enhanced degree of freedom based on the coprime planar array tensor model so as to achieve accurate direction-of-arrival estimation in the underdetermined case.

SUMMARY

An objective of the present disclosure is to provide a two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing with respect to the problem of loss of degrees-of-freedom in the existing methods, which provides an effective solution for establishing a relationship between the two-dimensional coarray and the tensor-based signals received by the coprime planar array, fully mining structural information of the two-dimensional coarray, and using structured coarray tensor construction and coarray tensor decomposition to achieve two-dimensional direction-of-arrival estimation in the underdetermined case.

The objective of the present disclosure is achieved through the following technical solution: a two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, including the following steps:

(1) deploying a coprime planar array with 4 M_(x)M_(y)+N_(x)N_(y)−1 physical sensors; wherein M_(x), N_(x) and M_(y), N_(y) are a pair of coprime integers respectively, and M_(x)<N_(x), M_(y)<N_(y); the coprime planar array can be decomposed into two sparse uniform subarrays

₁ and

₂;

(2) assuming that there are K far-field narrowband incoherent sources from directions {(θ₁, φ₁), (θ₂, φ₂), . . . , (θ_(K), φ_(K))}, the received signal of the sparse uniform subarray

₁ of the coprime planar array can be expressed as a three-dimensional tensor X₁ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×L) (L denotes the number of snapshots):

1 = ∑ k = 1 K ⁢ a M ⁢ x ⁡ ( θ k , φ k ) ∘ a M ⁢ y ⁡ ( θ k , φ k ) ∘ s k + 1 ,

where s_(k)=[s_(k,1), s_(k,2), . . . , s_(k,L)]^(T) denotes a signal waveform corresponding to the k^(th) source, [⋅]^(T) denotes a transpose operation, ∘ denotes an exterior product of vectors,

₁ denotes an additive Gaussian white noise tensor, and a_(Mx)(θ_(k), φ_(k)) and a_(My)(θ_(k), φ_(k)) denote the steering vectors of

₁ along the x-axis and the y-axis, respectively. a_(Mx)(θ_(k), φ_(k)) and a_(My)(θ_(k), φ_(k)) are defined as:

$\begin{matrix} {{{a_{Mx}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,\ e^{{- j}\pi u_{1}^{(2)}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}},\cdots\mspace{14mu},e^{{- j}\pi u_{1}^{({2M_{x}})}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}}} \right\rbrack^{T}},{{a_{My}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\pi v_{1}^{(2)}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}},\cdots\mspace{14mu},e^{{- j}\;\pi\;{v_{1}{({2M_{y}})}}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}}} \right\rbrack^{T}},} & \; \end{matrix}$

where u₁ ^((i) ¹ ⁾ (i₁=1, 2, . . . , 2M_(x)) and v₁ ^((i) ² ⁾ (i₂=1, 2, . . . , 2M_(y)) denote the positions of the i₁ ^(th) and i₂ ^(th) sensor in the sparse subarray

₁ along the x-axis and the y-axis with u₁ ⁽¹⁾=0, v₁ ⁽¹⁾=0, j=√{square root over (−1)};

denoting the received signals of the sparse uniform subarray

₂ by another three-dimensional tensor X₂ϵ

^(N) ^(x) ^(×N) ^(y) ^(×L):

2 = ∑ k = 1 K ⁢ a N ⁢ x ⁡ ( θ k , φ k ) ∘ a N ⁢ y ⁡ ( θ k , φ k ) ∘ k + 2 ,

where

₂ denotes a noise tensor, and a_(Nx)(θ_(k), φ_(k)) and a_(Ny)(θ_(k), φ_(k)) denote the steering vectors of V₂ along the x-axis and the y-axis respectively, which are defined as:

a_(Nx)(θ_(k), φ_(k)) = [1, e^(−jπu₂⁽²⁾sin (φ_(k))cos (θ_(k))), ⋯  , e^(−jπu₂^((2N_(x)))sin (φ_(k))cos (θ_(k)))]^(T), a_(Ny)(θ_(k), φ_(k)) = [1, e^(−jπv₂⁽²⁾sin (φ_(k))sin (θ_(k))), ⋯  , e^(−j π v₂(2N_(y))sin (φ_(k))sin (θ_(k)))]^(T),

where u₂ ^((i) ³ ⁾ (i₃=1, 2, . . . , N_(x)) and v₂ ^((i) ⁴ ⁾ (i₄=1, 2, . . . , N_(y)) denote the positions of the i₃ ^(th) and i₄ ^(th) sensor in the sparse subarray

₂ along the x-axis and the y-axis with u₂ ⁽¹⁾=0, v₂ ⁽¹⁾=0;

calculating the second-order cross-correlation tensor

ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×N) ^(x) ^(×N) ^(y) of the two three-dimensional tensor signals X₁ and X₂:

= 1 L ⁢ ∑ l = 1 L ⁢ 1 ⁢ ( l ) ∘ 2 * ⁢ ( l ) ,

where X₁(l) and X₂(l) denote the l^(th) slice of X₁ and X₂ along the third dimension (i.e., temporal dimension) respectively, and (⋅)* denotes a conjugate operation;

(3) deriving an augmented discontinuous virtual planar array

from the cross-correlation tensor

, where the position of each virtual sensor can be defined as:

={(M _(x) n _(x) d+N _(x) m _(x) d,−M _(y) n _(y) d+N _(y) m _(y) d)|0≤n _(x) ≤N _(x)−1,0≤m _(x)≤2M _(x)−1,0≤n _(y) ≤N _(y)−1,0≤m _(y)≤2M _(y)−1},

where the spacing d is set to half of the signal wavelength λ, i.e., d=λ/2; S contains a virtual uniform planar array

including (M_(x)N_(x)+M_(x)+N_(x)−1)×(M_(y)N_(y)+M_(y)+N_(y)−1) virtual sensors with distributing from (−N_(x)+1)d to (M_(x)N_(x)+M_(x)−1)d in the x-axis and from (−N_(y)+1)d to (M_(y)N_(y)+M_(y)−1)d in the y-axis, which is defined as:

={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x)+1≤p _(x) ≤M _(x) N _(x) +M _(x)−1, −N _(y)+1≤p _(y) ≤M _(y) N _(y) +M _(y)−1.},

defining dimension sets

₁={1, 3} and

₂={2, 4}, and reshaping the cross-correlation tensor

(noiseless scene) with {

₁,

₂} to obtain an equivalent second-order signal Uϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×N) ^(x) ^(×N) ^(y) of the augmented virtual planar array

, which is ideally modeled as:

U

=Σ _(k=1) ^(K)σ_(x) ² a _(x)(θ_(k),φ_(k))·a _(y)(θ_(k),φ_(k)),

where a_(x)(θ_(k), φ_(k))=a*_(Nx)(θ_(k), φ_(k)) a_(Mx)(θ_(k), φ_(k)), a_(y)(θ_(k), φ_(k))=a*_(Ny)(θ_(k), φ_(k)) a_(My)(θ_(k), φ_(k)) denote steering vectors of the augmented virtual planar array

along the x axis and they axis, σ_(k) ² denotes power of a k^(th) source, and ⊗ denotes Kroneker product; the equivalent signal Ũϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ of the virtual uniform planar array

is obtained by selecting elements in U that corresponds to the virtual sensor positions in

. Ũ is modeled as:

Ũ=Σ _(k=1) ^(K)σ_(k) ² b _(x)(θ_(k) ,φk)·b _(y)(θ_(k),φ_(k)),

where b_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] and b_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, e^(−jπ(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] are the steering vectors of the virtual uniform planar array

along the x axis and they axis;

(4) taking the symmetric part of the virtual uniform planar array

, i.e.,

into account, which is defined as:

={{hacek over (x)},{hacek over (y)})|{hacek over (x)}={hacek over (p)} _(x) d,{hacek over (y)}={hacek over (p)} _(y) d,−M _(x) N _(x) −M _(x)+1≤{hacek over (p)} _(x) ≤N _(x)−1, −M _(y) N _(y) −M _(y)+1≤{hacek over (p)} _(y) ≤N _(y)−1}.

transforming elements in the equivalent signal Ũ of the virtual uniform planar array

, to obtain an equivalent signal Ũ_(sym)ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ of the symmetric uniform planar array

, which is defined as:

Ũ _(sym)=Σ_(k=1) ^(K)σ_(k) ²(b _(x)(θ_(k),φ_(k))e ^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾)·(b _(y)(θ_(k),φ_(k))e ^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾),

where e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾ and e^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾) are the symmetric factors in the x-axis and y-axis respectively

concatenating the equivalent signals Ũ of the virtual uniform planar array

and the equivalent signals Ũ_(sym) of the symmetric virtual uniform planar array

along the third dimension, to obtain a three-dimensional coarray tensor

ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ^(−1)×2), which is defined as:

${= {\sum\limits_{k = 1}^{K}{\sigma_{k}^{2}{{b_{x}\left( {\theta_{k},\varphi_{k}} \right)} \circ {b_{y}\left( {\theta_{k},\varphi_{k}} \right)} \circ {h_{k}\left( {\theta_{k},\varphi_{k}} \right)}}}}},$

where h_(k) (θ_(k), φ_(k))=[1, e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(+N) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ^()+(−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(+N) ^(y) ^()sin(φ) ^(y) ^()sin(θ) ^(y) ⁾]^(T) denotes the symmetric factor vector;

(5) segmenting a subarray with a size of P_(x)×P_(y) from the virtual uniform planar array

, and dividing the virtual uniform planar array

into L_(x)×L_(y) partially overlapped uniform subarrays; denoting the subarray by

_((s) _(x) _(, s) _(y) ₎, s_(x)=1, 2, . . . , L_(x), s_(y)=1, 2, . . . , L_(y), and expressing the position of the virtual sensor in

_((s) _(x) _(,s) _(y) ₎ as:

_((s) _(x) _(,s) _(y) ₎={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x) +s _(x) ≤p _(x) ≤N _(x) +s _(x) +P _(x)−1, −N _(y) +s _(y) ≤p _(y) ≤−N _(y) +s _(y) +P _(y)−1}.

obtaining a sub-coarray tensor

_((s) _(x) _(, s) _(y) ₎ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2) corresponding to

_((s) _(x) _(, s) _(y) ₎ by selecting elements in the coarray tensor

according to the positions of virtual sensors in the sub array

_((s) _(x) _(, s) _(y) ₎:

_((s) _(x) _(,s) _(y) ₎=Σ_(k=1) ^(K)σ_(k) ²(c _(x)(θ_(k),φ_(k))e ^((s) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾)·(c _(y)(θ_(k),φ_(k))e ^((s) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾ ·h _(k)(θ_(k),φ_(k)),

where c_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . ,

e^(−jπ(−N) ^(x) ^(+P) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] and c_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾,

e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, . . . , e^(−jπ(−N) ^(y) ^(+P) ^(y) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] are the steering vectors of the virtual subarray

_((1,1)) along the x axis and they axis; after the above operations, a total of L_(x)×L_(y) three-dimensional sub-coarray tensors

_((s) _(x) _(, s) _(y) ₎ whose dimensions are all P_(x)×P_(y)×2 are obtained; the sub-coarray tensors

_((s) _(x) _(, s) _(y) ₎ with the same index subscript s_(y) are concatenated along the fourth dimension, to obtain L_(y) four-dimensional tensors of size P_(x)×P_(y)×2×L_(x); and the L_(y) four-dimensional tensors are further concatenated along the fifth dimension, to obtain a five-dimensional tensor

ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2×L) ^(x) ^(×L) ^(y) , which is defined as:

=Σ_(k−1) ^(K)σ_(k) ² c _(x)(θ_(k),φ_(k))·c _(y)(θ_(k),φ_(k))·h _(k)(θ_(k),φ_(k))·d _(x)(θ_(k),φ_(k))·d _(y)(θ_(k),φ_(k)),

where d_(x)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾], d_(y)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] are the shifting factor vectors along the x-axis and the y-axis, respectively;

(6) defining dimensional sets

₁={1, 2},

₂={3},

₃={4, 5}, by reshaping

with {

₁,

₂,

₃}, i.e., combining the first and second dimensions of the five-dimensional tensor

, combining the fourth and fifth dimensions, and retaining the third dimension, a three-dimensional structured coarray tensor

ϵ

^(P) ^(x) ^(P) ^(y) ^(×2×L) ^(x) ^(L) ^(y) is obtained as:

=Σ_(k=1) ^(K)σ_(k) ² g(θ_(k),φ_(k))·h(θ_(k),φ_(k))·f(θ_(k),φ_(k)),

where g(θ_(k), φ_(k))=c_(y)(θ_(k), φ_(k))⊗c_(x)(θ_(k), φ_(k)), f(θ_(k), φ_(k))=d_(y)(θ_(k), φ_(k))⊗d_(x)(θ_(k), φ_(k)); and

(7) performing CANDECOMP/PARACFAC decomposition on the three-dimensional structured coarray tensor

, to obtain a closed-form solution of two-dimensional direction-of-arrivals in the underdetermined case.

Further, the structure of the coprime planar array in step (1) is specifically described as follows: a pair of sparse uniform planar subarrays

₁ and

₂ are constructed on a coordinate system xoy, where

₁ includes 2M_(x)×2M_(y) sensors, the spacing between sensors in the x-axis direction and the spacing in the y-axis direction are N_(x)d and N_(y)d respectively, and the sensor coordinates on the xoy plane are {(N_(x)dm_(x), N_(y)dm_(y)), m_(x)=0, 1, . . . , 2M_(x)−1, m_(y)=0, 1, . . . , 2M_(y)−1};

₂ includes N_(x)×N_(y) sensors, the spacing between sensors in the x-axis direction and array element spacing in the y-axis direction are M_(x)d and M_(y)d respectively, and the sensor coordinates on the xoy plane are {(M_(x)dn_(x), M_(y)dn_(y)), n_(x)=0, 1, . . . , N_(x)−1, n_(y)=0, 1, . . . , N_(y)−1}; herein, M_(x), N_(x) and M_(y), N_(y) are a pair of coprime integers respectively, and M_(x)≤N_(x), M_(y)≤N_(y); since the subarray

₁ and

₂ only overlap at the origin of the coordinate system (0,0), the coprime planar array includes 4M_(x)M_(y)+N_(x)N_(y)−1 physical sensors.

Further, the cross-correlation tensor

in step (3) is ideally modeled (noiseless scene) as:

=Σ_(k=1) ^(K)σ_(k) ² a _(Mx)(θ_(k),φ_(k))·a _(My)(θ_(k),φ_(k))·a* _(Nx)(θ_(k),φ_(k))·a* _(Ny)(θ_(k),φ_(k))

a_(Mx)(θ_(k), φ_(k))·a*_(Nx)(θ_(k), φ_(k)) in the cross-correlation tensor

can derive an augmented coarray along the x axis, and a_(My)(θ_(k), φ_(k))·a*_(Ny)(θ_(k), φ_(k)) can derive an augmented coarray along the y axis, so as to obtain the augmented discontinuous virtual planar array

.

Further, the equivalent signals of the symmetric

in step (4) is obtained by the transformation of the equivalent signals Ũ of the virtual uniform planar array

, which specifically includes: performing a conjugate operation on Ũ to obtain Ũ*, and flipping elements in Ũ* left and right and then up and down, to obtain the equivalent signals Ũ_(sym) of the symmetric uniform planar array

.

Further, the concatenation of the equivalent signals Ũ of

and the equivalent signals Ũ_(sym) of

along the third dimension, to obtain a three-dimensional coarray tensor

in step (4) includes: performing CANDECOMP/PARACFAC decomposition on

to achieve two-dimensional direction-of-arrival estimation in the underdetermined case.

Further, in step (7), CANDECOMP/PARAFAC decomposition is performed in the three-dimensional structured coarray tensor

, to obtain three factor matrixes, G=[g({circumflex over (θ)}₁, {circumflex over (φ)}₁), g({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , g({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], H=[h({circumflex over (θ)}₁, {circumflex over (φ)}₁), h({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , h({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], F=[f({circumflex over (θ)}₁, {circumflex over (φ)}₁), f({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , f({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))]; where ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)), k=1, 2, . . . , K is the estimations of (θ_(k), φ_(k)), k=1, 2, . . . , K; elements in the second row in the factor matrix G are divided by elements in the first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾, and elements in the P_(x)+1^(th) row in the factor matrix G are divided by elements in the first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾; after a similar parameter retrieval operation from the factor matrix F, averaging and logarithm processing are performed on parameters extracted from G and F respectively, to obtain û_(k)=sin({circumflex over (φ)}_(k))cos({circumflex over (θ)}_(k)) and {circumflex over (v)}_(k)=sin({circumflex over (φ)}_(k))sin({circumflex over (θ)}_(k)), and then the closed-form solution of the two-dimensional azimuth and elevation angles ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)) is:

θ ^ k = arctan ⁡ ( k k ) . ⁢ φ ^ k = k 2 + k 2 .

in the above step, CANDECOMP/PARAFAC decomposition follows the following unique condition:

_(rank)(G)+

_(rank)(H)+

_(rank)(F)≥2K+2,

where

_(rank)(⋅) denotes a Kruskal's rank of a matrix, and

_(rank)(G)=min(P_(x)P_(y), K),

_(rank)(H)=min(L_(x)L_(y), K),

_(rank)(F)=min(2, K), min(⋅) denotes a minimization operation;

optimal P_(x) and P_(y) values are obtained according to the above inequality, so as to obtain a theoretical maximum value of K, i.e., a theoretical upper bound of distinguishable sources, is obtained by ensuring that the uniqueness condition is satisfied; herein, the value of K exceeds the total number of physical sensors in the coprime planar array 4M_(x)M_(y)+N_(x)N_(y)−1.

Compared with the prior art, the present disclosure has the following advantages:

(1) In the present disclosure, the received signals of a coprime planar array are represented by a tensor, which is different from the technical means of representing two-dimensional space information by vectorization and averaging snapshot information to obtain the correlation statistics in the traditional matrix method. In the present disclosure, snapshot information is superimposed in a third dimension, and a cross-correlation tensor including four-dimensional space information is obtained through cross-correlation statistical analysis of tensor signals, which saves space structure information of original multidimensional signals.

(2) In the present disclosure, coarray statistics are derived from a four-dimensional cross-correlation tensor, and dimensions in the cross-correlation tensor that represent coarray information in the same direction are combined, so as to derive the equivalent signals of the augmented virtual arrays, which overcomes that the coarray equivalent signal derived by the traditional matrix method has problems such as loss of structural information and a large linear scale.

(3) In the present disclosure, a three-dimensional tensor signal is further constructed in a coarray on the basis of constructing the equivalent signals of the virtual array, so as to establish an association between a two-dimensional coarray and the tensorial space, which provides a theoretical pre-condition for obtaining a closed-form solution of two-dimensional direction-of-arrival estimation by tensor decomposition and also lays a foundation for the construction of a structured coarray tensor and the increase of degrees-of-freedom.

(4) In the present disclosure, the number of degrees-of-freedom of the coarray tensor processing method is effectively improved by dimension extension of the coarray tensor signal and the construction of the structured coarray tensor, thereby achieving two-dimensional direction-of-arrival estimation in the underdetermined case.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an overall flow diagram according to the present disclosure;

FIG. 2 is a schematic structural diagram of a coprime planar array according to the present disclosure;

FIG. 3 is a schematic structural diagram of an augmented virtual planar array derived according to the present disclosure;

FIG. 4 is a schematic diagram of a dimension extension process of a coarray tensor signal of a coprime planar array according to the present disclosure; and

FIG. 5 is an effect diagram of multi-source direction-of-arrival estimation in a method according to the present disclosure.

DESCRIPTION OF EMBODIMENTS

The technical solution of the present disclosure will be described in further detail below with reference to the accompanying drawings.

In order to solve the problem of loss of degrees-of-freedom in the existing methods, the present disclosure provides a two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, which establishes an association between a coprime planar array coarray domain and second-order tensor statistics in combination with means such as multi-linear analysis, coarray tensor signal construction, and coarray tensor decomposition, so as to achieve two-dimensional direction-of-arrival estimation in an underdetermined condition. Referring to FIG. 1, the present disclosure is implemented through the following steps:

Step 1: A coprime planar array is deployed. The coprime planar array is deployed with 4 M_(x)M_(y)+N_(x)N_(y)−1 physical sensors at a receiving end. As shown in FIG. 2, a pair of sparse uniform planar subarrays

₁ and

₂ are constructed on a coordinate system xoy plane, wherein

₁ includes 2M_(x)×2M_(y) sensors, spacing in the x-axis direction and spacing in the y-axis direction are N_(x)d and N_(y)d respectively, and position coordinates thereof on the xoy are {(N_(x)dm_(x), N_(y)dm_(y)), m_(x)=0, 1, . . . , 2M_(x)−1, m_(y)=0, 1, . . . , 2M_(y)−1};

₂ includes N_(x)×N_(y) sensors, spacing in the x-axis direction and spacing in the y-axis direction are M_(x)d and M_(y)d respectively, and position coordinates thereof on the xoy are {(M_(x)dn_(x), M_(y)dn_(y)), n_(x)=0, 1, . . . , N_(x)−1, n_(y)=0, 1, . . . , N_(y)−1}, M_(x), N_(x) and M_(y), N_(y) are a pair of coprime integers respectively, and M_(x)<N_(x), M_(y)<N_(y). A spacing d is set to half of an incident narrowband signal wavelength λ, i.e., d=λ/2. Subarray combination is performed on

₁ and

₂ according to overlap of sensors at a position of a coordinate system (0,0), to obtain a coprime planar array actually including 4M_(x)M_(y)+N_(x)N_(y)−1 physical sensors.

Step 2: The tensor signals of the coprime planar array is modeled. Assuming that there are K far-field narrowband incoherent sources from {(θ₁, φ₁), (θ₂, φ₂), . . . , (θ_(K), φ_(K))} directions, a three-dimensional tensor X₁ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×L) (L denotes the number of sampling snapshots) may be obtained after sampling snapshots on the sparse uniform subarray

₁ of the coprime planar array are superimposed in the third dimension, which is modeled as:

1 = ∑ k = 1 K ⁢ a M ⁢ x ⁡ ( θ k , φ k ) ∘ a M ⁢ y ⁡ ( θ k , φ ) ∘ k + 1 ,

wherein S_(k)=[s_(k,1), s_(k,2), . . . , s_(k,L)]^(T) denotes a multi-snapshot signal waveform corresponding to the k^(th) source, [⋅]^(T) denotes a transpose operation, ∘ denotes an exterior product of vectors,

₁ denotes an additive Gaussian white noise tensor, and a_(Mx)(θ_(k), φ_(k)) and a_(My)(θ_(k), φ_(k)) denote steering vectors of

₁ in x-axis and y-axis directions respectively, corresponding to the source from direction (θ_(k), φ_(k)), and are defined as:

$\begin{matrix} {{{a_{Mx}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\;\pi\; u_{1}^{(2)}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; u_{1}^{({2{Mx}})}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}}} \right\rbrack^{T}},{{a_{My}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\pi v_{1}^{(2)}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; v_{1}^{({2{My}})}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}}} \right\rbrack^{T}},} & \; \end{matrix}$

wherein u₁ ^((i) ¹ ⁾(i₁=1, 2, . . . , 2M_(x)) and v₁ ^((i) ² ⁾(i₂=1, 2, . . . , 2M_(y)) denote actual positions of i₁ ^(th) and i₂ ^(th) physical sensors in the sparse subarray

₁ in the x-axis and y-axis directions, and u₁ ⁽¹⁾=0, v₁ ⁽¹⁾)=0, j=√{square root over (−1)}.

Similarly, a received signals of the sparse uniform subarray

₂ may be defined by another three-dimensional tensor X₂ϵ

^(N) ^(x) ^(×N) ^(y) ^(×L):

2 = ∑ k = 1 K ⁢ a N ⁢ x ⁡ ( θ k , φ k ) ∘ a N ⁢ y ⁡ ( θ k , φ k ) ∘ s k + 2 ,

wherein

₂ denotes a noise tensor, and a_(Nx)(θ_(k), φ_(k)) and a_(Ny)(θ_(k), φ_(k)) denote the steering vectors of

₂ in the x-axis and y-axis directions respectively, corresponding to a signal source from direction (θ_(k), φ_(k)), and are defined as:

$\begin{matrix} {{{a_{Nx}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\;\pi\; u_{2}^{(2)}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; u_{2}^{({Nx})}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}}} \right\rbrack^{T}},{{a_{Ny}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\pi v_{2}^{(2)}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; v_{2}^{({Ny})}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}}} \right\rbrack^{T}},} & \; \end{matrix}$

wherein u₂ ^((i) ³ ⁾(i₃=1, 2, . . . , N_(x)) and v₂ ^((i) ⁴ ⁾(i₄=1, 2, . . . , N_(y)) denote actual positions of i₃ ^(th) and i₄ ^(th) physical sensors in the sparse subarray

₂ in the x-axis and y-axis directions, and u₂ ⁽¹⁾=0, v₂ ⁽¹⁾=0.

Cross-correlation statistics of three-dimensional tensors X₁ and X₂ sampled by the sparse subarrays

₁ and

₂ is calculated, to obtain the second-order cross-correlation tensor

ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×N) ^(x) ^(×N) ^(y) including four-dimensional spatial information:

= 1 L ⁢ ∑ l = 1 L ⁢ 1 ⁢ ( l ) ∘ 2 * ⁢ ( l ) ,

wherein X₁(l) and X₂(l) denote the l^(th) slice of X₁ and X₂ in the third dimension (i.e., temporal dimension) respectively, and (⋅)* denotes a conjugate operation.

Step 3: A second-order equivalent signals of the virtual array associated with coprime planar array based on cross-correlation tensor statistics is derived. The cross-correlation tensor

of the received tensor signals of the two subarrays may be ideally modeled (noiseless scene) as:

=Σ_(k−1) ^(K)σ_(k) ² a _(Mx)(θ_(k),φ_(k))·a _(My)(θ_(k),φ_(k))·a* _(Nx)(θ_(k),φ_(k))·a* _(Ny)(θ_(k),φ_(k)),

wherein σ_(k) ² denotes power of the k^(th) source. In this case, a_(Mx)(θ_(k), φ_(k))·a*_(Nx)(θ_(k), φ_(k)) in the cross-correlation tensor

is equivalent to an augmented coarray along the x axis, and a_(My)(θ_(k), φ_(k))·a*_(Ny)(θ_(k), φ_(k)) is equivalent to an augmented coarray along the y axis, so as to obtain the augmented discontinuous virtual planar array

. As shown in FIG. 3, a position of each virtual sensor is defined as:

={(−M _(x) n _(x) d+N _(x) m _(x) d,−M _(y) n _(y) d+N _(y) m _(y) d)|0≤n _(x) ≤N _(x)−1,0≤m _(x)≤2M _(x)−1,0≤n _(y) ≤N _(y)−1,0≤m _(y)≤2M _(y)−1}.

contains a virtual uniform planar array

including (M_(x)N_(x)+M_(x)+N_(x)−1)×(M_(y)N_(y)+M_(y)+N_(y)−1) virtual sensors with x-axis distribution from (−N_(x)+1)d to (M_(x)N_(x)+M_(x)−1)d and y-axis distribution from (−N_(y)+1)d to (M_(y)N_(y)+M_(y)−1)d, as shown in the dashed box of FIG. 3, which is specifically defined as:

={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x)+1≤p _(x) ≤M _(x) N _(x) +M _(x)−1, −N _(y)+1≤p _(y) ≤M _(y) N _(y) +M _(y)−1}.

In order to obtain the equivalent signals of the augmented virtual planar array

, there is a need to combine the first and third dimensions in the cross-correlation tensor

that represent the spatial information in the x-axis direction into one dimension and combine second and fourth dimensions that represent spatial information in the y-axis direction into another dimension. Dimension combination of tensors can be achieved by the tensor reshaping operation. Taking a four-dimensional tensor

ϵ

¹ ¹ ^(×I) ² ^(×I) ³ ^(×I) ⁴ =Σ_(r=1) ^(R)b₁₁·b₁₂·b₂₁·b₂₂ as an example, dimension sets

₁={1, 2} and

₂={3, 4} are defined, and then unfolding of a module {

₁,

₂} of PARAFAC decomposition of

is as follows:

B ∈ I 1 ⁢ I 2 × I 3 ⁢ I 4 ⁢ = Δ ⁢ { 𝕋 1 , 𝕋 12 } = ∑ τ = 1 R ⁢ b 1 ∘ b 2 ,

wherein the tensor subscript denotes the tensor reshaping; b₁=b₁₂ ⊗b₁₁ and b₂=b₂₂ ⊗b₂₁ denote factor vectors of two dimensions after the unfolding respectively. Herein, ⊗ denotes Kroneker product. Therefore, dimension sets

₁={1, 3} and

₂={2, 4} are defined, and a module {

₁,

₂} of reshaping is performed for an ideal value

(noiseless scene) of the cross-correlation tensor

, to obtain an equivalent second-order signals Uϵ

^(2M) ^(x) ^(N) ^(x) ^(×2M) ^(y) ^(N) ^(y) of the augmented virtual planar array

:

U

Σ _(k=1) ^(K)σ_(k) ² a _(x)(θ_(k),φ_(k))·a _(y)(θ_(k),φ_(k)),

wherein a_(x)(θ_(k), φ_(k))=a*_(Nx)(θ_(k), φ_(k))⊗a_(Mx)(θ_(k), φ_(k)), a_(y)(θ_(k), φ_(k))=a*_(Ny)(θ_(k), φ_(k))⊗a_(My)(θ_(k), φ_(k)) denote steering vectors of the augmented virtual planar array

corresponding to a (θ_(k), φ_(k)) direction on the x axis and the y axis. Based on the above derivation, the equivalent signals Ũϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ of the virtual uniform planar array

is obtained by selecting elements in U corresponding to virtual sensor positions in

, which is modeled as:

${\overset{\sim}{U} = {\sum\limits_{k = 1}^{K}{\sigma_{k}^{2}{{b_{x}\left( {\theta_{k},\varphi_{k}} \right)} \circ {b_{y}\left( {\theta_{k},\varphi_{k}} \right)}}}}},$

where b_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . ,

e^(−jπ(M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] and b_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾,

e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, e^(−jπ(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] Denote steering vectors of the virtual uniform planar array

corresponding to the (θ_(k), φ_(k)) direction on the x axis and the y axis.

Step 4: A three-dimensional tensor signal of the coprime planar array virtual domain is constructed. In order to increase an effective aperture of the virtual planar array and further improve the degree of freedom, a symmetric extension

of the virtual uniform planar array

is taken into account, which is defined as:

={{hacek over (x)},{hacek over (y)})|{hacek over (x)}={hacek over (p)} _(x) d,{hacek over (y)}={hacek over (p)} _(y) d,−M _(x) N _(x) −M _(x)+1≤{hacek over (p)} _(x) ≤N _(x)−1, −M _(y) N _(y) −M _(y)+1≤{hacek over (p)} _(y) ≤N _(y)−1}.

In order to obtain the equivalent signals of the symmetric uniform planar array

, the equivalent signal Ũ of the virtual uniform planar array

may be transformed specifically as follows: performing a conjugate operation on Ũ to obtain Ũ*, and flipping elements in Ũ* left and right and then up and down, to obtain the equivalent signal Ũ_(sym)ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ corresponding to the symmetric uniform planar array

, which is defined as:

Ũ _(sym)=Σ_(k=1) ^(K)σ_(k) ²(b _(x)(θ_(k),φ_(k))e ^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾)·(b _(y)(θ_(k),φ_(k))e ^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾),

where e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾ and e^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾) denote symmetric factors in the x-axis and y-axis directions respectively when mirror transformation is performed on the virtual uniform planar array

.

The equivalent signals Ũ of the virtual uniform planar array

and the equivalent signal Ũ_(sym) of the symmetric uniform planar array

are superimposed in the third dimension, to obtain a three-dimensional coarray tensor

ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ^(−1)×2) for the coprime planar array, the structure thereof is as shown in FIG. 4, and the three-dimensional coarray tensor is defined as:

${= {\sum\limits_{k = 1}^{K}{\sigma_{k}^{2}{{b_{x}\left( {\theta_{k},\varphi_{k}} \right)} \circ {b_{y}\left( {\theta_{k},\varphi_{k}} \right)} \circ {h_{k}\left( {\theta_{k},\varphi_{k}} \right)}}}}},$

wherein h_(k) (θ_(k), φ_(k))=[1, e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(+N) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ^()+(−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(+N) ^(y) ^()sin(φ) ^(y) ^()sin(θ) ^(y) ⁾]^(T) denotes symmetric transformation factor vector.

Step 5: A five-dimensional coarray tensor is constructed based on a coarray tensor dimension extension strategy. As shown in FIG. 4, a subarray with a size of P_(x)×P_(y) is taken, from the virtual uniform planar array

, every other sensor along the x-axis and y-axis directions respectively, and then the virtual uniform planar array

may be divided into L_(x)×L_(y) uniform subarrays partially overlapping each other. L_(x), L_(y), P_(x), P_(y) satisfy the following relations:

P _(x) +L _(x)−1=M _(x) N _(x) +M _(x) +N _(x)−1,

P _(y) +L _(y)−1=M _(y) N _(y) +M _(y) +N _(y)−1.

The subarray is defined as

_((s) _(x) _(,s) _(y) ₎, s_(x)=1, 2, . . . , L_(x), s_(y)=1, 2, . . . , L_(y), and a position of an virtual sensor in

_((s) _(x) _(,s) _(y) ₎ is defined as:

_((s) _(x) _(,s) _(y) ₎={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x) +s _(x) ≤p _(x) ≤−N _(x) +s _(x) +P _(x)−1, −N _(y) +s _(y) ≤p _(y) ≤−N _(y) +s _(y) +P _(y)−1}.

A tensor signal

_((s) _(x) _(,s) _(y) ₎ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2) in the virtual subarray

_((s) _(x) _(,s) _(y) ₎ is obtained according to corresponding position elements in a coarray tensor signal

corresponding to the subarray

_((s) _(x) _(,s) _(y) ₎.

_((s) _(x) _(,s) _(y) ₎=Σ_(k=1) ^(K)σ_(k) ²(c _(x)(θ_(k),φ_(k))e ^((s) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾)·(c _(y)(θ_(k),φ_(k))e ^((s) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾ ·h _(k)(θ_(k),φ_(k)),

where c_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . ,

e^(−jπ(−N) ^(x) ^(+P) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] and c_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾,

e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, . . . , e^(−jπ(−N) ^(y) ^(+P) ^(y) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] denote steering vectors of a virtual subarray

_(1,1)) corresponding to the (θ_(k), φ_(k)) direction on the x axis and they axis. After the above operations, a total of L_(x)×L_(y) three-dimensional tensors

_((s) _(x) _(, s) _(y) ₎ whose dimensions are all P_(x)×P_(y)×2 are obtained. In order to extend the dimension of the coarray tensor, firstly, tensors in the three-dimensional sub-coarray tensors

_((s) _(x) _(, s) _(y) ₎ with the same index subscript s_(y) are concatenated in the fourth dimension, to obtain L_(y) four-dimensional tensors with size of P_(x)×P_(y)×2×L_(x); and further, the L_(y) four-dimensional tensors are concatenated in the fifth dimension, to obtain a five-dimensional coarray tensor

ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2×L) ^(x) ^(×L) ^(y) which is defined as:

=Σ_(k=1) ^(K)σ_(k) ² c _(x)(θ_(k),φ_(k))·c _(y)(θ_(k),φ_(k))·h _(k)(θ_(k),φ_(k))·d _(x)(θ_(k),φ_(k))·d(θ_(k),φ_(k)),

where d_(x)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾], d_(y)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] denote the shifting factor vectors corresponding to the x-axis and y-axis directions respectively during coarray tensor dimension extension and construction.

Step 6: A structured coarray tensor including three-dimensional spatial information is formed. In order to obtain the structured coarray tensor, the five-dimensional coarray tensor

after dimension extension is combined along first and second dimensions representing angular information and is also combined along fourth and fifth dimensions representing shifting information, and the third dimension representing symmetric transformation information is retained, which includes the following specific operations: defining dimension sets

₁={1, 2},

₂={3},

₃={4, 5}, and unfolding a module {

₁,

₂,

₃} of reshaping of

, to obtain a three-dimensional structured coarray tensor

ϵ

^(P) ^(x) ^(P) ^(y) ^(×2×L) ^(x) ^(L) ^(y) :

=Σ_(k=1) ^(K)σ_(k) ² g(θ_(k),φ_(k))·h(θ_(k),φ_(k))·f(θ_(k),φ_(k)),

where g(θ_(k), φ_(k))=c_(y)(θ_(k), φ_(k))⊗c_(x)(θ_(k), φ_(k)), f(θ_(k), φ_(k))=d_(y)(θ_(k), φ_(k))⊗d_(x)(θ_(k), φ_(k)). Three dimensions of the structured coarray tensor

represent angular information, symmetric transformation information, and shifting information respectively.

Step 7: Two-dimensional direction-of-arrival estimation is obtained through CANDECOMP/PARACFAC decomposition of the structured coarray tensor. CANDECOMP/PARACFAC decomposition is performed on the three-dimensional structured coarray tensor

, to obtain three factor matrixes, G=[g({circumflex over (θ)}₁, {circumflex over (φ)}₁), g({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , g({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], H=[h({circumflex over (θ)}₁, {circumflex over (φ)}₁), h({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , h({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], F=[f({circumflex over (θ)}₁, {circumflex over (φ)}₁), f({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , f({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))]; where ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)), k=1, 2, . . . , K is the estimated value of each incident angle (θ_(k), φ_(k)), k=1, 2, . . . , K; elements in the second row in the factor matrix G are divided by elements in the first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾, and elements in the P_(x)+1^(th) row in the factor matrix G are divided by elements in the first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾; after a similar parameter retrieval operation is also performed on the factor matrix F, averaging and logarithm processing are performed on parameters extracted from G and F respectively, to obtain û_(k)=sin({circumflex over (φ)}_(k))cos({circumflex over (θ)}_(k)) and {circumflex over (v)}_(k)=sin({circumflex over (φ)}_(k))sin({circumflex over (θ)}_(k)), and then the closed-form solution of the two-dimensional direction-of-arrival estimation ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)) is:

θ ^ k = arctan ⁡ ( k ⁢ k ) , ⁢ φ ^ k = k 2 + k 2 .

In the above step, CANDECOMP/PARAFAC decomposition follows the following uniqueness condition:

_(rank)(G)+

_(rank)(H)+

_(rank)(F)≥2K+2,

wherein

_(rank)(⋅) denotes a Kruskal's rank of a matrix, and

_(rank)(G)=min(P_(x)P_(y), K),

_(rank)(H)=min(L_(x), L_(y), K),

_(rank)(F)=min(2, K), min(⋅) denotes a minimization operation.

Optimal P_(x) and P_(y) values are obtained according to the above inequality, so as to obtain a theoretical maximum value of K, i.e., a theoretical upper bound of the distinguishable sources, is obtained by ensuring that the uniqueness condition is satisfied. Herein, the value of K exceeds the total number 4M_(x)M_(y)+N_(x)N_(y)−1 of actual physical sensors of the coprime planar array due to construction and processing of the structured coarray tensor, which indicates that the degrees-of-freedom of direction-of-arrival estimation is improved.

The effect of the present disclosure is further described below with reference to a simulation example.

Simulation example: a coprime planar array is used to receive incident signals, parameters thereof are selected as M_(x)=2, M_(y)=3, N_(x)=3, N_(y)=4, that is, the coprime planar array includes a total of 4M_(x)M_(y)+N_(x)N_(y) 1=35 physical sensors. Assuming that the number of incident narrowband sources is 50 and azimuth angles in an incident direction are evenly distributed over [−65°, 5°]∪[5°, 65° ], elevation angles are evenly distributed within a space angle domain range of [5°, 65° ]. 500 noiseless sampling snapshots are used for a simulation experiment.

Estimation results of the two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing provided in the present disclosure are as shown in FIG. 5, among which x and y axes represent elevation and azimuth angles of incident signal sources respectively. It can be seen that the method provided in the present disclosure can effectively distinguish the 50 incident sources. For the traditional direction-of-arrival estimation method using a uniform planar array, 35 physical sensors can be used to distinguish only at most 34 incident signals. The above results indicate that the method provided in the present disclosure achieves the increase of the degree of freedom.

Based on the above, the present disclosure fully considers an association between a two-dimensional coarray of a coprime planar array and the tensorial space, derives the coarray equivalent signals through second-order statistic analysis of the tensor signal, and retains structural information of the multi-dimensional received signal and the coarray. Moreover, coarray tensor dimension extension and structured coarray tensor construction mechanisms are established, which lays a theoretical foundation for maximizing the number of degrees-of-freedom. Finally, the present disclosure performs multidimensional feature extraction on the structured coarray tensor to form a closed-form solution of two-dimensional direction-of-arrival estimation, and achieves a breakthrough in the degree of freedom performance.

The above are only preferred implementations of the present disclosure. Although the present disclosure has been disclosed above with preferred embodiments, the preferred embodiments are not intended to limit the present disclosure. Any person skilled in the art can make, without departing from the scope of the technical solution of the present disclosure, many possible variations and modifications to the technical solution of the present disclosure or modify the technical solution as equivalent embodiments of equivalent changes by using the method and technical contents disclosed above. Therefore, any simple alteration, equivalent change, or modification made to the above embodiments according to the technical essence of the present disclosure without departing from the contents of the technical solution of the present disclosure still fall within the protection scope of the technical solution of the present disclosure. 

What is claimed is:
 1. A two-dimensional direction-of-arrival (DOA) estimation method for a coprime planar array based on structured coarray tensor signal processing, comprising following steps of: (1) deploying a coprime planar array with 4 M_(x)M_(y)+N_(x)N_(y)−1 physical sensors; wherein M_(x), N_(x) and M_(y), N_(y) are pairs of coprime integers respectively, and M_(x)≤N_(x), M_(y)<N_(y); the coprime planar array is decomposed into two sparse uniform subarrays

₁ and

₂; (2) assuming that there are K far-field narrowband incoherent sources from directions {(θ₁, φ₁), (θ₂, φ₂), . . . , (θ_(K), φ_(K))}, the received signals of the sparse uniform subarray

₁ of the coprime planar array is expressed by a three-dimensional tensor X₁ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×L) (L denotes a number of sampling snapshots): 1 = ∑ k = 1 K ⁢ a M ⁢ x ⁡ ( θ k , φ k ) ∘ a M ⁢ y ⁡ ( θ k , φ k ) ∘ s k + 1 , wherein s_(k)=[s_(k,1), s_(k,2), . . . , s_(k,L)]^(T) denotes a waveform corresponding to the k^(th) source, [⋅]^(T) denotes a transpose operation, ∘ denotes an exterior product of vectors,

₁ denotes an additive Gaussian white noise tensor, and a_(Mx)(θ_(k), φ_(k)) and a_(My)(θ_(k), φ_(k)) denote steering vectors of

₁ along x-axis and y-axis, respectively, a_(Mx)(θ_(k), φ_(k)) and a_(My)(θ_(k), φ_(k)) are defined as: $\begin{matrix} {{{a_{Mx}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\;\pi\; u_{1}^{(2)}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; u_{1}^{({2{Mx}})}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}}} \right\rbrack^{T}},{{a_{My}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\pi v_{1}^{(2)}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; v_{1}^{({2{My}})}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}}} \right\rbrack^{T}},} & \; \end{matrix}$ wherein u₁ ^((i) ¹ ⁾(i₁=1, 2, . . . , 2M_(x)) and v₁ ^((i) ² ⁾(i₂=1, 2, . . . , 2M_(y)) denote positions of i₁ ^(th) and i₂ ^(th) physical sensors in the sparse subarray

₁ in the x-axis and y-axis with u₁ ⁽¹⁾=0, v₁ ⁽¹⁾=0, j=√{square root over (−1)}; expressing a received signal of the sparse uniform subarray

₂ as another three-dimensional tensor X₂ϵ

^(N) ^(x) ^(×N) ^(y) ^(×L): 2 = ∑ k = 1 K ⁢ a N ⁢ x ⁡ ( θ k , φ k ) ∘ a N ⁢ y ⁡ ( θ k , φ k ) ∘ s k + 2 , wherein

₂ denotes a noise tensor, and a_(Nx)(θ_(k), φ_(k)) and a_(Ny)(θ_(k), φ_(k)) denote steering vectors of

₂ in the x-axis and y-axis respectively, which are defined as: $\begin{matrix} {{{a_{Nx}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\;\pi\; u_{2}^{(2)}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; u_{2}^{({Nx})}{\sin{(\varphi_{k})}}{\cos{(\theta_{k})}}}} \right\rbrack^{T}},{{a_{Ny}\left( {\theta_{k},\varphi_{k}} \right)} = \left\lbrack {1,e^{{- j}\pi v_{2}^{(2)}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}},\ldots\mspace{14mu},e^{{- j}\;\pi\; v_{2}^{({Ny})}{\sin{(\varphi_{k})}}{\sin{(\theta_{k})}}}} \right\rbrack^{T}},} & \; \end{matrix}$ wherein u₂ ^((i) ³ ⁾(i₃=1, 2, . . . , N_(x)) and v₂ ^((i) ⁴ ⁾(i₄=1, 2, . . . , N_(y)) denote positions of i₃ ^(th) and i₄ ^(th) physical sensors in the sparse subarray

₂ in the x-axis and y-axis with u₂ ⁽¹⁾=0, v₂ ⁽¹⁾=0; calculating a second-order cross-correlation tensor

ϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×N) ^(x) ^(×N) ^(y) of two three-dimensional tensor signals X₁ and X₂: = 1 L ⁢ ∑ l = 1 L ⁢ 1 ⁢ ( l ) ∘ 2 * ⁢ ( l ) , wherein X₁(l) and X₂(l) denote an l^(th) slice of X₁ and X₂ along a third dimension (i.e., a temporal dimension) respectively, and (⋅)* denotes a conjugate operation; (3) deriving an augmented discontinuous virtual planar array

from the cross-correlation tensor

, wherein a position of each virtual sensor is defined as:

={(−M _(x) n _(x) d+N _(x) m _(x) d,−M _(y) n _(y) d+N _(y) m _(y) d)|0≤n _(x) ≤N _(x)−1,0≤m _(x)≤2M _(x)−1,0≤n _(y) ≤N _(y)−1,0≤m _(y)≤2M _(y)−1}, wherein a spacing d is set to half of a signal wavelength λ, i.e., d=λ/2;

comprises a virtual uniform planar array

comprising (M_(x)N_(x)+M_(x)+N_(x)−1)×(M_(y)N_(y)+M_(y)+N_(y)−1) virtual sensors with distributing from (−N_(x)+1)d to (M_(x)N_(x)+M_(x)−1)d in x-axis and distributing from (−N_(y)+1)d to (M_(y)N_(y)+M_(y)−1)d in y-axis, the virtual uniform planar array

is defined as:

={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x)+1≤p _(x) ≤M _(x) N _(x) +M _(x)−1, −N _(y)+1≤p _(y) ≤M _(y) N _(y) +M _(y)−1}, defining dimension sets

₁={1, 3} and

₂={2, 4}, and reshaping cross-correlation tensor

(noiseless scene) with {

₁,

₂} to obtain the equivalent second-order signals Uϵ

^(2M) ^(x) ^(×2M) ^(y) ^(×N) ^(x) ^(×N) ^(y) of the augmented virtual planar array

, which is ideally modeled as: U

=Σ _(k=1) ^(K)σ_(k) ² a _(x)(θ_(k),φ_(k))·a _(y)(θ_(k),φ_(k)), wherein a_(x)(θ_(k), φ_(k))=a*_(Nx)(θ_(k), φ_(k))⊗a_(Mx)(θ_(k), φ_(k)), a_(y)(θ_(k), φ_(k))=a*_(Ny)(θ_(k), φ_(k))⊗a_(My)(θ_(k), φ_(k)) denote steering vectors of the augmented virtual planar array

along the x axis and the y axis, σ_(k) ² denotes power of a k^(th) source, and ⊗ denotes Kroneker product; an equivalent signal Ũϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ of the virtual uniform planar array

is obtained by selecting elements in U corresponding to virtual sensor positions in

, Ũ is modeled as: Ũ=Σ _(k=1) ^(K)σ_(k) ² b _(x)(θ_(k),φ_(k))·b _(y)(θ_(k),φ_(k)), where b_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] and b_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, . . . , e^(−jπ(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] denote steering vectors of the virtual uniform planar array

along the x axis and they axis; (4) taking a symmetric part

of the virtual uniform planar array

into account, the symmetric

is defined as:

={{hacek over (x)},{hacek over (y)})|{hacek over (x)}={hacek over (p)} _(x) d,{hacek over (y)}={hacek over (p)} _(y) d,−M _(x) N _(x) −M _(x)+1≤{hacek over (p)} _(x) ≤N _(x)−1, −M _(y) N _(y) −M _(y)+1≤{hacek over (p)} _(y) ≤N _(y)−1}, transforming elements in the equivalent received signal Ũ of the virtual uniform planar array

, to obtain the equivalent signals Ũ_(sym)ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ⁻¹⁾ of a symmetric uniform planar array

, which is defined as: Ũ _(sym)=Σ_(k=1) ^(K)σ_(k) ²(b _(x)(θ_(k),φ_(k))e ^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾)·(b _(y)(θ_(k),φ_(k))e ^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾), where e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾ and e^((−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(N) ^(x) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾) are symmetric factors in the x-axis and y-axis, respectively concatenating the equivalent signals Ũ of the virtual uniform planar array

and the equivalent signals Ũ_(sym) of the symmetric uniform planar array

along the third dimension, to obtain a three-dimensional tensor

ϵ

^((M) ^(x) ^(N) ^(x) ^(+M) ^(x) ^(+N) ^(x) ^(−1)×(M) ^(y) ^(N) ^(y) ^(+M) ^(y) ^(+N) ^(y) ^(−1)×2) for the coprime planar array, the three-dimensional coarray tensor

is defined as: ${= {\sum\limits_{k = 1}^{K}{\sigma_{k}^{2}{{b_{x}\left( {\theta_{k},\varphi_{k}} \right)} \circ {b_{y}\left( {\theta_{k},\varphi_{k}} \right)} \circ {h_{k}\left( {\theta_{k},\varphi_{k}} \right)}}}}},$ wherein h_(k) (θ_(k), φ_(k))=[1, e^((−M) ^(x) ^(N) ^(x) ^(−M) ^(x) ^(+N) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ^()+(−M) ^(y) ^(N) ^(y) ^(−M) ^(y) ^(+N) ^(y) ^()sin(φ) ^(y) ^()sin(θ) ^(y) ⁾]^(T) denotes a symmetric factor vector; (5) segmenting, a subarray with a size of P_(x)×P_(y) from the virtual uniform planar array

, and dividing the virtual uniform planar array

into L_(x)×L_(y) partially overlapped uniform subarrays; denoting the subarray by

_((s) _(x) _(, s) _(y) ₎, s_(x)=1, 2, . . . , L_(x), s_(y)=1, 2, . . . , L_(y), and expressing a position of an virtual sensor in

_((s) _(x) _(, s) _(y) ₎ as:

_((s) _(x) _(,s) _(y) ₎={(x,y)|x=p _(x) d,y=p _(y) d,−N _(x) +s _(x) ≤p _(x) ≤−N _(x) +s _(x) +P _(x)−1, −N _(y) +s _(y) ≤p _(y) ≤−N _(y) +s _(y) +P _(y)−1}, obtaining a sub-coarray tensor

_((s) _(x) _(, s) _(y) ₎ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2) in the virtual subarray

_((s) _(x) _(, s) _(y) ₎ by selecting elements in the coarray stensor

according to position of virtual sensors in the sub array

_((s) _(x) _(, s) _(y) ₎:

_((s) _(x) _(,s) _(y) ₎=Σ_(k=1) ^(K)σ_(k) ²(c _(x)(θ_(k),φ_(k))e ^((s) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾)·(c _(y)(θ_(k),φ_(k))e ^((s) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾ ·h _(k)(θ_(k),φ_(k)), where c_(x)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(x) ^(+1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, e^(−jπ(−N) ^(x) ^(+2)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(−N) ^(x) ^(+P) ^(x) ^()sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] and c_(y)(θ_(k), φ_(k))=[e^(−jπ(−N) ^(y) ^(+1)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, e^(−jπ(−N) ^(y) ^(+2)sin(φ) ^(k) ^()sin(θ) ^(k) ⁾, . . . , e^(−jπ(−N) ^(y) ^(+P) ^(y) ^()sin(φ) ^(k) ^()sin(θ) ^(k) ⁾] are steering vectors of a virtual subarray

_((1,1)) along the x axis and they axis; after the above operations, a total of L_(x)×L_(y) three-dimensional sub-coarray tensors

_((s) _(x) _(, s) _(y) ₎ with dimensions being all P_(x)×P_(y)×2 are obtained; the sub-coarray tensors

_((s) _(x) _(, s) _(y) ₎ with a same index subscript s_(y) are concatenated along a fourth dimension, to obtain L_(y) four-dimensional tensors of size P_(x)×P_(y)×2×L_(x); and the L_(y) four-dimensional tensors are further concatenated along a fifth dimension, to obtain a five-dimensional tensor

ϵ

^(P) ^(x) ^(×P) ^(y) ^(×2×L) ^(x) ^(×L) ^(y) , the five-dimensional coarray tensor

is defined as:

=Σ_(k−1) ^(K)σ_(k) ² c _(x)(θ_(k),φ_(k))·c _(y)(θ_(k),φ_(k))·h _(k)(θ_(k),φ_(k))·d _(x)(θ_(k),φ_(k))·d _(y)(θ_(k),φ_(k)), where d_(x)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(x) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾], d_(y)(θ_(k), φ_(k))=[1, e^(−jπ sin(φ) ^(k) ^()cos(θ) ^(k) ⁾, . . . , e^(−jπ(L) ^(y) ^(−1)sin(φ) ^(k) ^()cos(θ) ^(k) ⁾] are shifting factor vectors along the x-axis and y-axis respectively; (6) defining dimensional sets

₁={1, 2},

₂={3},

₃={4, 5}, by reshaping

with {

₁,

₂,

₃}, i.e., combining first and second dimensions of the five-dimensional tensor

, combining fourth and fifth dimensions, and retaining the third dimension, a three-dimensional structured coarray tensor

ϵ

^(P) ^(x) ^(P) ^(y) ^(×2×L) ^(x) ^(L) ^(y) is obtained as:

=Σ_(k=1) ^(K)σ_(k) ² g(θ_(k),φ_(k))·h(θ_(k),φ_(k))·f(θ_(k),φ_(k)), where g(θ_(k), φ_(k))=c_(y)(θ_(k), φ_(k))⊗c_(x)(θ_(k), φ_(k)), f(θ_(k), φ_(k))=d_(y)(θ_(k), φ_(k))⊗d_(x)(θ_(k), φ_(k)) represent the angular information and the shifting information, respectively; (7) performing CANDECOMP/PARACFAC decomposition on the three-dimensional structured coarray tensor

, to obtain a closed-form solution of two-dimensional direction-of-arrivals in the underdetermined case.
 2. The two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing according to claim 1, wherein the structure of the coprime planar array in step (1) is specifically described as follows: a pair of sparse uniform planar subarrays

₁ and

₂ is constructed on a coordinate system xoy, wherein

₁ comprises 2M_(x)×2M_(y) sensors, the spacing in the x-axis direction and the spacing in the y-axis direction are N_(x)d and N_(y)d, respectively, and sensor coordinates thereof on the xoy plane are {(N_(x)dm_(x), N_(y)dm_(y)), m_(x)=0, 1, . . . , 2M_(x)−1, m_(y)=0, 1, . . . , 2M_(y)−1};

₂ comprises N_(x)×N_(y) sensors, the spacing in the x-axis direction and the spacing in the y-axis direction are M_(x)d and M_(y)d, respectively, and the sensor coordinates on the xoy plane are {(M_(x)dn_(x), M_(y) dn_(y)), n_(x)=0, 1, . . . , N_(x)−1, n_(y)=0, 1, . . . , N_(y)−1}; M_(x), N_(x) and M_(y), N_(y) are a pair of coprime integers respectively, and M_(x)<N_(x), M_(y)<N_(y); since the subarray

₁ and

₂ only overlap at an origin of the coordinate system (0,0), a coprime planar array comprising 4M_(x)M_(y)+N_(x)N_(y)−1 physical sensors.
 3. The two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing according to claim 1, wherein the cross-correlation tensor

in step (3) is ideally modeled (noiseless scene) as:

=Σ_(k−1) ^(K)σ_(k) ² a _(Mx)(θ_(k),φ_(k))·a _(My)(θ_(k),φ_(k))·a* _(Nx)(θ_(k),φ_(k))·a* _(Ny)(θ_(k),φ_(k)), a_(Mx)(θ_(k), φ_(k))·a*_(Mx)(θ_(k), φ_(k)) in the cross-correlation tensor

derives augmented coarray along the x axis, and a_(My)(θ_(k), φ_(k))·a*_(Ny)(θ_(k), φ_(k)) derives an augmented coarray along the y axis, so as to obtain the augmented discontinuous virtual planar array

.
 4. The two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing according to claim 1, wherein the equivalent signals of the symmetric

of the virtual uniform planar array

in step (4) is obtained by transformation of the equivalent signals Ũ of the virtual uniform planar array

, which specifically comprises: performing a conjugate operation on Ũ to obtain Ũ*, and flipping elements in Ũ* left and right and then up and down, to obtain the equivalent signals Ũ_(sym) of the symmetric uniform planar array

.
 5. The two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing according to claim 1, wherein the concatenation of the equivalent signals Ũ

and the equivalent signals Ũ_(sym) of

along the third dimension, to obtain a three-dimensional coarray tensor

in step (4) comprises: performing CANDECOMP/PARACFAC decomposition on

to achieve two-dimensional direction-of-arrival estimation in the overdetermined case.
 6. The two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing according to claim 1, wherein in step (7), CANDECOMP/PARAFAC decomposition is performed on the three-dimensional structured coarray tensor

, to obtain three factor matrixes, G=[g({circumflex over (θ)}₁, {circumflex over (φ)}₁), g({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , g({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], H=[h({circumflex over (θ)}₁, {circumflex over (φ)}₁), h({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , h({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))], F=[f({circumflex over (θ)}₁, {circumflex over (φ)}₁), f({circumflex over (θ)}₂, {circumflex over (φ)}₂), . . . , f({circumflex over (θ)}_(K), {circumflex over (φ)}_(K))]; wherein ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)), k=1, 2, . . . , K is an estimation of (θ_(k), φ_(k)), k=1, 2, . . . , K; elements in a second row in the factor matrix G are divided by elements in a first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾, elements in the P_(x)+1^(th) row in the factor matrix G are divided by elements in the first row to obtain e^(−jπ sin({circumflex over (φ)}) ^(k) ^()cos({circumflex over (θ)}) ^(k) ⁾, after a similar parameter retrieval operation on the factor matrix F, averaging and logarithm processing are performed to parameters extracted from G and F, respectively, to obtain û_(k)=sin({circumflex over (φ)}_(k))cos({circumflex over (θ)}_(k)) and {circumflex over (v)}_(k)=sin({circumflex over (φ)}_(k))sin({circumflex over (θ)}_(k)), and then a closed-form solution of the two-dimensional azimuth and elevation angles ({circumflex over (θ)}_(k), {circumflex over (φ)}_(k)) is: θ ^ k = arctan ⁡ ( k k ) , ⁢ φ ^ k = k 2 + k 2 , in the above step, CANDECOMP/PARAFAC decomposition follows a uniqueness condition:

_(rank)(G)+

_(rank)(H)+

_(rank)(F)≥2K+2, wherein

_(rank)(⋅) denotes a Kruskal rank of a matrix, and

_(rank)(G)=min(P_(x)P_(y), K),

_(rank)(H)=min(L_(x)L_(y), K),

_(rank)(F)=min(2, K), min(⋅) denotes a minimization operation; optimal P_(x) and P_(y) values are obtained according to the above inequality, so as to obtain a theoretical maximum value of K, i.e., a theoretical upper bound of the number of distinguishable sources, is obtained by ensuring that the uniqueness condition is satisfied; the value of K exceeds the total number of physical sensors in the coprime planar array 4M_(x)M_(y)+N_(x)N_(y)−1. 